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Estimation and Applications of Quantile Regression for Binary Longitudinal Data

Mohammad Arshad Rahman () and Angela Vossmeyer

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Abstract: This paper develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as well as multivariate heterogeneity associated with several covariates. The methodology is applied to study female labor force participation and home ownership in the United States. The results offer new insights at the various quantiles, which are of interest to policymakers and researchers alike.

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Date: 2019-09
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Published in Advances in Econometrics, Volume 40B, 2019

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